Training Uncertainty-Aware Classifiers with Conformalized Deep Learning

• URL: http://arxiv.org/abs/2205.05878v1
• Date: Thu, 12 May 2022 05:08:10 GMT
• Title: Training Uncertainty-Aware Classifiers with Conformalized Deep Learning
• Authors: Bat-Sheva Einbinder, Yaniv Romano, Matteo Sesia, Yanfei Zhou
• Abstract summary: Deep neural networks are powerful tools to detect hidden patterns in data and leverage them to make predictions, but they are not designed to understand uncertainty. We develop a novel training algorithm that can lead to more dependable uncertainty estimates, without sacrificing predictive power.
• Score: 7.837881800517111
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deep neural networks are powerful tools to detect hidden patterns in data and leverage them to make predictions, but they are not designed to understand uncertainty and estimate reliable probabilities. In particular, they tend to be overconfident. We address this problem by developing a novel training algorithm that can lead to more dependable uncertainty estimates, without sacrificing predictive power. The idea is to mitigate overconfidence by minimizing a loss function, inspired by advances in conformal inference, that quantifies model uncertainty by carefully leveraging hold-out data. Experiments with synthetic and real data demonstrate this method leads to smaller conformal prediction sets with higher conditional coverage, after exact calibration with hold-out data, compared to state-of-the-art alternatives.

Related papers

• Confidence Intervals and Simultaneous Confidence Bands Based on Deep Learning [0.36832029288386137]
  We provide a valid non-parametric bootstrap method that correctly disentangles data uncertainty from the noise inherent in the adopted optimization algorithm. The proposed ad-hoc method can be easily integrated into any deep neural network without interfering with the training process.
  arXiv  Detail & Related papers  (2024-06-20T05:51:37Z)
• Error-Driven Uncertainty Aware Training [7.702016079410588]
  Error-Driven Uncertainty Aware Training aims to enhance the ability of neural classifiers to estimate their uncertainty correctly. The EUAT approach operates during the model's training phase by selectively employing two loss functions depending on whether the training examples are correctly or incorrectly predicted. We evaluate EUAT using diverse neural models and datasets in the image recognition domains considering both non-adversarial and adversarial settings.
  arXiv  Detail & Related papers  (2024-05-02T11:48:14Z)
• Uncertainty Quantification in Deep Neural Networks through Statistical Inference on Latent Space [0.0]
  We develop an algorithm that exploits the latent-space representation of data points fed into the network to assess the accuracy of their prediction. We show on a synthetic dataset that commonly used methods are mostly overconfident. In contrast, our method can detect such out-of-distribution data points as inaccurately predicted, thus aiding in the automatic detection of outliers.
  arXiv  Detail & Related papers  (2023-05-18T09:52:06Z)
• Fast Uncertainty Estimates in Deep Learning Interatomic Potentials [0.0]
  We propose a method to estimate the predictive uncertainty based on a single neural network without the need for an ensemble. We demonstrate that the quality of the uncertainty estimates matches those obtained from deep ensembles.
  arXiv  Detail & Related papers  (2022-11-17T20:13:39Z)
• Reliability-Aware Prediction via Uncertainty Learning for Person Image Retrieval [51.83967175585896]
  UAL aims at providing reliability-aware predictions by considering data uncertainty and model uncertainty simultaneously. Data uncertainty captures the noise" inherent in the sample, while model uncertainty depicts the model's confidence in the sample's prediction.
  arXiv  Detail & Related papers  (2022-10-24T17:53:20Z)
• Learning Uncertainty with Artificial Neural Networks for Improved Predictive Process Monitoring [0.114219428942199]
  We distinguish two types of learnable uncertainty: model uncertainty due to a lack of training data and noise-induced observational uncertainty. Our contribution is to apply these uncertainty concepts to predictive process monitoring tasks to train uncertainty-based models to predict the remaining time and outcomes.
  arXiv  Detail & Related papers  (2022-06-13T17:05:27Z)
• Dense Uncertainty Estimation [62.23555922631451]
  In this paper, we investigate neural networks and uncertainty estimation techniques to achieve both accurate deterministic prediction and reliable uncertainty estimation. We work on two types of uncertainty estimations solutions, namely ensemble based methods and generative model based methods, and explain their pros and cons while using them in fully/semi/weakly-supervised framework.
  arXiv  Detail & Related papers  (2021-10-13T01:23:48Z)
• Improving Uncertainty Calibration via Prior Augmented Data [56.88185136509654]
  Neural networks have proven successful at learning from complex data distributions by acting as universal function approximators. They are often overconfident in their predictions, which leads to inaccurate and miscalibrated probabilistic predictions. We propose a solution by seeking out regions of feature space where the model is unjustifiably overconfident, and conditionally raising the entropy of those predictions towards that of the prior distribution of the labels.
  arXiv  Detail & Related papers  (2021-02-22T07:02:37Z)
• Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions [121.10450359856242]
  We develop a frequentist procedure that utilizes influence functions of a model's loss functional to construct a jackknife (or leave-one-out) estimator of predictive confidence intervals. The DJ satisfies (1) and (2), is applicable to a wide range of deep learning models, is easy to implement, and can be applied in a post-hoc fashion without interfering with model training or compromising its accuracy.
  arXiv  Detail & Related papers  (2020-06-29T13:36:52Z)
• Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [100.52588638477862]
  We develop an approximate Bayesian inference scheme based on posterior regularisation. We demonstrate the utility of our method in the context of transferring prognostic models of prostate cancer across globally diverse populations.
  arXiv  Detail & Related papers  (2020-06-26T13:50:19Z)
• Uncertainty Estimation Using a Single Deep Deterministic Neural Network [66.26231423824089]
  We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass. We scale training in these with a novel loss function and centroid updating scheme and match the accuracy of softmax models.
  arXiv  Detail & Related papers  (2020-03-04T12:27:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.